Security LLM Training for Semantic Similarity in Cyber Logs
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Solution Overview
Problem
Existing machine learning systems trained on natural language data struggle to accurately capture the semantic meaning of cybersecurity logs and threat intelligence documents due to differences in language building blocks, leading to inefficiencies in anomaly detection and information generation.
Innovation Solution
Training a security-specific large language model (LLM) with security-specific objectives and datasets, incorporating similarity deduplication and long line handling, to improve the capture of semantic meaning in cybersecurity logs and threat intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning systems trained on natural language data are used, then the system can process general language data, but it struggles to accurately capture the semantic meaning of cybersecurity logs and threat intelligence documents
Solution Approach 1:
The patent applies local quality by creating a security-specific language model that is optimized for cybersecurity domains while maintaining the general capabilities of large language models. The model is fine-tuned on security-specific datasets (security logs, threat intelligence documents, vulnerability reports) to capture domain-specific semantic patterns, syntax, and terminology. This allows the system to have high measurement precision for cybersecurity content while retaining adaptability for other domains through the base model's general language understanding capabilities.
2Measurement precision
If a security-specific large language model is trained with security-specific objectives and datasets, then the performance and accuracy of security information generation improves, but the energy consumption and computational costs increase
Solution Approach 1:
The patent applies preliminary action by pre-training the language model on comprehensive security datasets (security logs, threat intelligence, vulnerability reports) and fine-tuning it on security-specific objectives before deployment. This preliminary training establishes a strong foundation that enables the model to achieve high detection accuracy with more efficient inference operations. The model is pre-adapted to security domains, reducing the computational burden during actual security analysis operations compared to using a general-purpose model.
Solution Approach 2:
The patent applies parameter changes by adjusting model architecture parameters and training parameters specifically for security applications. The model uses security-specific tokenization, embedding dimensions, and training hyperparameters optimized for security data characteristics. These parameter adjustments enable the model to achieve high accuracy on security tasks while being more computationally efficient than applying generic large language model parameters to security problems.
3Measurement precision
If similarity deduplication and long line handling are incorporated in dataset generation, then the model's ability to capture semantic similarity improves, but the data processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing security logs into manageable lines or segments during data preprocessing. The system handles long log lines by segmenting them into smaller units while preserving semantic relationships. Similarity deduplication is performed on these segmented units, comparing them against existing segments to identify and remove duplicates. This segmentation approach enables accurate similarity capture without requiring the system to process entire lengthy log files at once, reducing computational complexity while maintaining precision.
Data Source
AI summary
Methods and apparatuses for improving the performance and energy efficiency of machine learning systems that generate security specific machine learning models and generate security related information using security specific machine learning models are described. A security specific machine learning model may comprise a security specific large language model (LLM). The security specific LLM may be trained and deployed to generate semantically related security information. The security specific LLM may be pretrained with a security specific data set that was generated using similarity deduplication and long line handling, and with security specific objectives, such as next log line prediction based on host, system, application, and cyber attacker behavior. The security specific large language model may be fine-tuned using a security specific similarity dataset that may be generated to align the security specific LLM to capture similarity between different security events.